Information
----Curriculum Vitae
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Research
----Publications
----Talks
 
Background
----Grants & Awards
----Brief Biosketch
 
Machine Learning at Toronto
learning.cs.toronto.edu
 
My Home Page
www.cs.toronto.edu/~rsalakhu
 
[Image]

 

Ruslan Salakhutdinov
PhD Student
Department of Computer Science
University of Toronto
6 King's College Rd.
Toronto, Ontario, Canada
M5S 3G4
Email: rsalakhu (at) cs (dot) toronto (dot) edu
Office: Pratt 265A

I am a PhD student at University of Toronto, where I work with Geoffrey Hinton and Sam Roweis.

ICML09 Workshop on Learning Feature Hierarchies here

  • Quick link to my research papers

  • Quick link to my software

    Reducing the Dimensionality of Data with Neural Networks.
    Geoffrey E. Hinton and Ruslan R. Salakhutdinov
    Science, 28 July 2006:
    Vol. 313. no. 5786, pp. 504 - 507
    [bibtex] [pdf][ Science Online]
    Supporting Online Material [pdf, Science Online]
    Matlab Code is available here

    Recent Papers

    John Langford, Ruslan Salakhutdinov and Tong Zhang.
    Learning Nonlinear Dynamic Models.
    Proceedings of the 26th International Conference on Machine Learning (ICML), 2009.
    [bibtex] [ps.gz][ pdf]

    Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov and David Mimno.
    Evaluation Methods for Topic Models.
    Proceedings of the 26th International Conference on Machine Learning (ICML), 2009.
    [bibtex] [ pdf]

    Ruslan Salakhutdinov and Geoffrey Hinton
    Deep Boltzmann Machines
    12th International Conference on Artificial Intelligence and Statistics (2009).
    [bibtex] [ps.gz][ pdf]

    Iain Murray and Ruslan Salakhutdinov
    Evaluating probabilities under high-dimensional latent variable models
    Advances in Neural Information Processing Systems 21 (2009).
    [bibtex] [ pdf]

    Ruslan Salakhutdinov
    Learning and Evaluating Boltzmann Machines
    Technical Report UTML TR 2008-002, Dept. of Computer Science, University of Toronto
    [bibtex] [ps.gz][ pdf]
    This paper introduces a new Boltzmann machine learning algorithm that combines variational techniques and MCMC.


    Recent Talks

  • Deep Boltzmann Machines (ps.gz, pdf).
    Snowbird workshop, April 2009.

  • I was co-organizing Deep Learning Workshop: Foundations and Future Directions, NIPS 2007.

  • Deep Belief Nets (ps.gz, pdf).
    Snowbird workshop, March 2007.
    Demo and explanation of Digits and Faces.

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    Ruslan Salakhutdinov, Artificial Intelligence Group, Toronto Computer Science || www.cs.toronto.edu/~rsalakhu